Often, production data is scattered across systems, with limited tools available to interpret trends or identify patterns. Process engineers may rely on manual methods or informal knowledge passed down through experience, which can lead to inconsistent results and delayed decision-making. Without structured analysis, issues such as unplanned downtime, yield loss, and poor product quality continue to impact production outcomes. To remain competitive, manufacturers need reliable systems that support timely, data-driven decisions across their operations.
The Opportunity
A leading global packaging manufacturer struggled with recurring production inefficiencies due to limited visibility and the lack of analytical insight into machine performance. Their diagnostic process was largely manual, heavily dependent on tribal knowledge, and often required days or even weeks to identify and replicate optimal production conditions, leading to prolonged disruptions and lost productivity. Engineering teams faced challenges in comparing historical production runs, detecting patterns, and uncovering meaningful correlations, which restricted their ability to make data-backed decisions and implement lasting improvements. The absence of centralized data, combined with the lack of analytical rigor, further hampered their ability to respond quickly, resolve issues systematically, and drive continuous operational excellence.
Impact
Our transformative solution enabled the client to shift from reactive troubleshooting to proactive optimization, unlocking new levels of operational excellence.
- $1M Additional Sales from Improved Yield & Quality: Optimized coating profiles and setpoints boosted output, quality, and efficiency
- 10% Lead Time Reduction: Enabled engineers to analyze machine trends and recipe deviations in minutes rather than days
- Knowledge Retention: Replaced tribal knowledge with data-backed insights for consistent operations across shifts
- Cross-functional Enablement: Maintenance, quality, and operations teams gained access to shared performance insights
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